The Decision Maker

Cooperative Data Analytics Through a Semantic Layer

Posted by Nate Wentzlaff on May 30, 2019 11:04:00 AM

Credit Unions Need to Establish a Common Language to Strengthen the Credit Union Movement

On a recent trip to Malaysia, I was able to play basketball with my brother-in-law (my wife is Malaysian). As we began the game, I realized that we were all relying on a single source of truth for the rules of basketball.  Even though we are from very different parts of the globe, we were operating under the same definitions of the rules of basketball. Imagine if we all began playing according to sources of the truth that dictated different ways to play basketball. Maybe my source of truth told me that I don’t need to dribble to play the game. The other team’s source of the truth dictated that they can tackle the other team. This game would end horribly and would probably escalate into a conflict quickly. The same is true for data analytics within the credit union movement today.

Different Sources of Truth

Within most credit unions, there are many different sources of truth. Marketing departments have their sources, Accounting has theirs, and Lending has as many sources as types of loans (i.e. credit cards, mortgages, student loans, etc.). Over the course of time, every department begins establishing their own language based on their sources of truth, which are usually centered around a specific source system. For example, the marketing team has an MCIF system, which has an abundance of data regarding households and members’ profiles. The lending department relies on its loan origination system, which displays information found within a member’s loan application. All the while, the contact center relies on their CRM, which houses data collected during calls with members. When there is a need to work together to accomplish a goal, these various departments come to a meeting speaking different languages and using a separate understanding of the rules of the credit union. Like the game of basketball without a common source of truth, the project or initiative often ends horribly.

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Topics: Data Integration, Semantic Layer, Collaboration

The CCPA and GDPR: How These Emerging Privacy Laws will Impact the Credit Union Industry: Part II

Posted by Alex Beversdorf on Apr 9, 2019 11:02:00 AM

In the first part of this blog, we discussed technology regulation and updates regarding the legislation. With that covered, Part II will focus on what it means for your credit union and how you can prepare for the changes. 

How the CCPA Will Change the Competitive Landscape in the US

The CCPA won’t apply to all companies but will apply to a great majority, especially if one of these three thresholds are met:

  • Gross annual revenues in excess of $25 million
  • Buys, receives, sells or shares the PII of 50,000 or more consumers, households or devices for the business’s commercial purpose
  • 50% or more of the businesses annual revenue comes from selling consumer’s PII

If any of the above conditions are met, the marketers of the effected company have a great deal of work to do. Especially if they have no business tactic or strategy in place to organize all of their customer specific data. To comply with the CCPA, marketers must be able to organize and develop an efficient data scheme that compiles all of their consumer data. Consumers have the right to:

  • Know what PII is being collected regarding them
  • Know whether that is being sold and to whom
  • Say no to the sale of their PII
  • Access their own PII
  • Equal service and pay from the company, even if they exercise their own privacy rights and it requires more work to be done on the side of the business
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Topics: Data Integration, Membership, Digital, Data Ownership

To Build or Not to Build (Buy) – That is the Question for Credit Unions

Posted by Peter Keers, PMP on Jan 31, 2019 1:52:49 PM

As the Age of Analytics for credit unions rolls forward, the question of “Build or Buy” is faced almost daily by decisionmakers. It comes at all stages in the data and analytics journey, so credit unions must understand the tradeoffs in deciding to Build or Buy.

First, however, consider the question itself: Build or Buy. “Build” means the credit union uses its own resources to design, construct, launch, and maintain an application or capability. “Buy” means acquiring these same elements from an entity outside the organization.

The fast pace of technological evolution has added an innovative dimension the definition of “Buy”. Increasingly, “Buy” includes Software as a Service (SaaS) as well as on-premises implementations.

The Build Option

The perceived advantages of Build are customization and control. By keeping projects in-house, the Credit Union can design a system tailored to its unique requirements. Although all credit unions are chartered to do a specific set of services, each has its own flavor for delivering these services.

These Build option advantages favor larger credit unions with greater resources. Having the team depth of a larger organization enables greater possibilities for having both the skills and numbers to take on Build projects.

The major disadvantage of Build is cost. A custom-tailored suit is more expensive than an off-the-rack brand. Another, subtle but important disadvantage is strategic focus. A credit union is wired to be a member-oriented financial services organization. Though it may have gifted technologists on its staff, most credit unions are unlikely to have the technical breadth and depth to build a truly industrial grade application. There is also a big risk of knowledge experts leaving the organization in the current low unemployment environment.

Another cost concern is ongoing maintenance and enhancements. Experience shows custom-built applications are notoriously expensive to keep up-to-date and in efficient working order. The credit union is saddled with this ongoing burden for its data and analytics capability to keep pace with new industry trends.

See 7 Challenges to Consider When Building a Data Warehouse: http://blog.onapproach.com/7-challenges-consider-building-data-warehouse

The Buy Option

At first glance, it might be assumed the Buy option is the mirror opposite of Build. A purchased product will not be exactly customized to the credit union’s specific requirements nor will the organization have as much control over the project. However, this is a game of trade-offs driven by primarily by the size of the credit union. In order to survive, all credit unions must embark on the data and analytics journey. Those ignoring this trend will ultimately be acquired by credit unions that do take data and analytics seriously or simply become obsolete.

For the majority of credit unions, the Buy option holds significant advantages. By giving up some customization and control, the organization gains significant data and analytics capabilities at a more affordable price. In fact, not only is a tested commercial product liable to cost less up front, it also has the advantage of having the bugs worked out as the result of use at multiple sites. Therefore, the cost and headaches of the inevitable errors in complex programming code are avoided. If fact, the perception that a Build project results in a more tailored outcome may be overstated. Most commercial products are very configurable to meet specific credit union requirements.

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Topics: Credit Unions, Data Integration, Insight Platform

The Cost of Building a Data Warehouse for an Analytics Platform

Posted by CU 2.0 on Jan 25, 2019 10:02:00 AM

Credit unions can benefit greatly from collecting and storing information to leverage Big Data. The cost of building a data warehouse can be steep, though. If you’re considering building a data warehouse for your credit union, it’s important to know what you’re getting yourself into.

The benefits of building a data warehouse speak for themselves in the financial world. Getting into the data analytics game isn’t cheap, however. It’s not as simple as just buying a data warehouse and watching a video tutorial; no, getting started requires a large initial investment as well as ongoing support and upkeep costs.

Here are a couple of the common issues associated with building a data warehouse for the credit union industry.

Initial Investment Costs

There are two major expense considerations for any enterprising credit union looking to construct its own data warehouse. The most pressing of the two is the financial cost, and the second is the time invested. Because we’re talking specifically about credit unions, let’s discuss the monetary side of this investment first.

For an individual credit union, the cost of building a data warehouse or data lake for an analytics platform starts at around $500,000 at the low end. Most data warehouses and data lakes run well over the million-dollar mark. While it’s certainly a worthwhile investment, it can also be prohibitively expensive for smaller, more community-focused credit unions.

 The second major cost factor is time, though we could also say that it costs patience as well. Regardless of the size of the warehouse and the experience of the people putting it together, building a data warehouse takes an average of two or three years. If you want an analytics platform immediately, then creating one in-house from the ground up might not be your best option.

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Topics: Data Integration, Analytic Data Model, Enterprise Data Management, Data Storage, Insight Platform

Why Credit Union Digital Transformation Can’t Work Without Credit Union Data Integration

Posted by CU 2.0 on Jan 17, 2019 11:01:00 AM

It’s no good to be a dinosaur in the financial sector. Not only are dinosaurs notoriously temperamental, but they can’t type. Oh, and they’re extinct. If branches don’t want to go the way of the dinosaur, then a little credit union digital transformation is their best hope.

(Hint: credit unions aren’t the only industry affected by digital transformation and the emerging primacy of data.)

While digital transformation is certainly the goal, it can’t just organically happen. Credit union digital transformation is a strategic process that incorporates several approaches, from digital engagement to data integration. In this blog, we’ll talk about the challenges of credit union data integration and collaborative analytics strategies.

Tying Together Data Sources

Typical credit unions have somewhere around six to eight data sources. Some have more. While having the data is certainly nice, it’s not much good to just sit on it.

Core and ancillary systems produce data at prodigious rates. These streams of data are all separate, too. Siloed data streams are great when you need to understand only the data produced by one source. However, individual sources of data have a nasty habit of not producing a clear, complete, actionable picture.

Making matters worse is that each system stores its data differently. If you want to perform data analysis on any of your credit union members, you have to check in on each system and pull different data sets from them.

This lack of robust credit union data integration hampers solid, actionable analytics. The first challenge for credit unions then is reconciling individual data streams into one single source of truth.

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Topics: Credit Unions, Data Integration, Digital

How Data Integration Helps Credit Union Analytics Platforms

Posted by CU 2.0 on Dec 11, 2018 10:59:00 AM

The immediate future of banking and the financial industry is in analytics. The ability to draw conclusions from massive sets of data helps financial institutions improve their advertisement targeting, their ability to underwrite loans, and a whole host of other things. One of the sticking points in the machinery is in credit unions’ ability to perform adequate data integration.

Data integration sounds relatively simple on its own. Data integration is the practice of combining multiple streams or forms of data into a single readable format. The extent of data integration needed increases as the amount of data—or the number of data sources—increases.

Why do Credit Unions Need Data Integration?

This will be a long answer, and so I’ll break it up into three parts. The first part will address the many sources of data that credit unions deal with on a daily basis. The second part will introduce the necessity of analytics platforms in finance. The third section will explain the role of credit union data integration in the grand scheme of things.

1.     Credit Unions Generate Lots of Data

Credit unions exist in the financial sector, which is technologically fast-moving. Partially because of this, and partially because credit unions must record financial and member data, credit unions are inundated with a massive amount of data daily.

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Topics: Data Integration, Data Analytics, Insight Platform

4 Challenges and the Opportunity for Credit Unions [Video]

Posted by Mark Portz on Feb 6, 2018 11:07:00 AM

In the webinar, "Fueling a Bright Future for Credit Union Analytics", Austin Wentzlaff, VP Business Development, OnApproach, presents the challenges and opportunities for credit unions regarding topics including data analytics, digital transformation, and collaboration. 

Credit unions are facing several unique challenges. As an industry, credit unions have fallen behind competing fintech startups and major retail banks. It is vital for financial institutions to understand the problems they are facing and how they are possible to overcome. In 2018, the credit union industry must work together to push past these challenges and remain relevant in the age of digital transformation.

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Topics: Data Integration, Data Pool, Video, Collaboration, Data Lake, Data Ownership

Lagging Contenders: How Credit Unions Can Catch Up in Data and Analytics - Part 1

Posted by Peter Keers, PMP on Aug 8, 2017 11:18:43 AM

The message has been ringing load and clear throughout the credit union industry for years: make better use of data and analytics or lose “member share” to more progressive CU peers or (horrors!) banks and fintech startups.

Despite the warning cries, the proportion of credit unions embracing this trend is (horrifyingly!) low.

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Topics: Data Integration, Analytic Data Model, Data Analytics, Data Quality

Stop Worrying about Millennials, Say Hello to Generation Z: Part 2

Posted by Mark Portz on May 9, 2017 1:04:00 PM

In the first part of this blog, we learned about who Generation Z is. Now that we have a better understanding of who we are talking about and realize that we need to be prepared, let’s look at what it means for your financial institution.

Key Takeaways for Financial Institutions

As an industry, we have spent a lot of time discussing millennials and their preferences. Millennials are driving a lot of change, but the next generation is going to be the real test. Generation Z is peeking around the corner and, as seen in part 1, is already showing new concerns around not only technology and convenience, but also privacy, security, marketing, and more.

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Topics: Data Integration, Digital

Prescriptive Analytics for Credit Unions: Healthcare Style

Posted by Nate Wentzlaff on Mar 2, 2017 12:03:00 PM

Just as healthcare is developing robust analytics for patients, credit unions have a great opportunity to empower members to track their financial health and take actions to improve it.

Being raised in a small town, I never thought about the healthcare I received. I had the same doctor from birth until I moved to college. As long as nothing seemed wrong to him, I felt confident that I was healthy. However, when I moved to a bigger city, everything changed. I was no longer able to rely on my hometown doctor, and I needed a way to monitor and maintain my health. At the same time, the healthcare industry was going through a data revolution. The traditional relationships between doctors and patients were changed forever. In shopping for my new healthcare provider, I felt the most comfortable with the one that had the best analytics and enabled me to make data-driven decisions to improve my health.

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Topics: Data Integration, Data Analytics